Perception is the cornerstone of cognition;memory, reasoning, and the formation of motor plans all rely on the ability to create a stable representation of the surrounding environment. However, neurons that encode sensory input are inherently noisy, so repeated presentations of a stimulus never evoke the same pattern of neural activity twice. In addition, more stimuli are typically present in the environment than the brain can simultaneously process, so relevant and irrelevant items must compete for cortical representation. Given these obstacles, the brain's ability to form coherent percepts is quite remarkable and understanding how this feat is accomplished is an important first step towards revealing the neural mechanisms that support conscious awareness. Theoretical studies suggest that perception is based on small populations of neurons that pool their output, since averaging reduces noise (termed population coding models). In addition, attending to specific locations or features biases neural activity so that relevant stimuli win representation at the expense of irrelevant stimuli. Thus, population coding schemes are necessary to provide a reliable foundation for perception, and selective attention is required to ensure that representations of relevant stimuli dominate awareness. Unfortunately, the relationship between attention and population codes is not well understood, in part because of technical limitations and in part because little work has been done to link single-unit attention modulations with the efficiency of information encoding at the population level. Here, we use a simple computational model to provide an explicit link between attention modulations, population codes and perception. To test predictions generated by the model, we use a combination of psychophysics and novel multivariate functional magnetic resonance imaging (fMRI) analysis techniques that are sensitive to changes in population response profiles across feature-selective regions of visual cortex. Specifically, we present a method for measuring feature-selective `tuning-functions'within very small regions of early visual cortex. These fMRI-based tuning functions resemble the tuning functions routinely obtained using single-unit recording methods in non-human primates, providing a powerful tool for evaluating theories of information encoding in human sensory cortices. In the first Specific Aim, we will use this collection of tools to test different models of attention gain (e.g. multiplicative gain vs. contrast gain) and to determine if feature-based attention systematically biases population response profiles in early visual cortex even before a stimulus is presented. In the Second Aim, we critically evaluate the common intuition that attention gain should be applied to sensory neurons that are maximally responsive to a target stimulus. Instead, we will test the counter-intuitive prediction that gain should sometimes be applied to neurons that are actually not tuned to the attended feature in order to maximize the efficiency of population codes. Together, these efforts will shed light on how the behavioral goals of an observer can shape population response profiles so that information processing within the visual system can be optimized. Since perception is a fundamental aspect of human information processing, our findings will be of interest to investigators focusing on perceptual learning, decision making, and memory. Moreover, the knowledge gained here will provide an important foundation for future applied research into disorders of attention such as Attention Deficit Disorder (ADD). For example, developing a better understanding about how attention modulates sensory neurons may lead to more objective diagnostic tests so that these disorders may be identified earlier and with greater accuracy. PUBLIC HEALTH RELEVANCE Whether listening to a teacher in a classroom or driving a car down the road, the ability to pay attention to important parts of the environment is critical to our success and survival. In the present research proposal, we seek to understand how patterns of neural activity in the brain support attentive behavior so that an observer may more efficiently understand and represent incoming sensory information. This knowledge will aid in the development of more objective tests for common disorders of attention - such as attention deficit disorder - so that diagnosis can proceed with greater precision and so that interventions can be started earlier.